284results about "Other databases clustering/classification" patented technology

A system may include a data classification module configured to classify data into classified data based on predefined sensitivity levels and regulatory compliance requirements. A system may include an access control module configured to manage permissions for different user roles within an enterprise, granting access to the classified data in accordance with the sensitivity levels and regulatory compliance requirements. A system may include a data formatting module configured to format classified data into formatted data with customized presentations for various enterprise departments. A system may include an integration module configured to interface with at least one of an Enterprise Resource Planning (ERP) system or a Customer Relationship Management (CRM) system to retrieve and classify the data. A system may include a user interface module configured to present the formatted data within the host application, providing a seamless user experience for accessing the embedded marketplace.

The application belongs to the technical field of complex network analysis and data mining, and particularly relates to a citation network high-order community detection method based on simplex. The method comprises the following steps: high-order simplex modeling of the citation network; partial order Hasse diagram construction; multi-step differentiable probability diffusion of the feature space; module degree optimization and unsupervised community division of the clustering space. The application introduces key modules such as partial order Hasse diagram modeling, multi-step differentiable probability diffusion of the feature space and clustering space module degree optimization, completely models entities and high-order interactions in the citation network, overcomes the problem of high-order topological semantic loss in traditional methods, and can realize accurate community division of academic citation networks in a label-free scene. The application can be widely applied to scenes such as academic citation network analysis, scientific research team mining and recommendation systems, and significantly improves the accuracy of high-order network community detection.

The application discloses a multi-target-based collaborative learning grouping and learning resource recommendation method and system, which comprises four steps of obtaining a data set, performing preliminary grouping by using an improved clustering grouping algorithm, grouping the remaining learners, and assigning learning resources to the formed groups for collaborative learning. Since the application adopts a multi-target-based hierarchical clustering algorithm for grouping, the course willingness, learning behavior habit and cognitive state of the group members are considered. Similar course willingness and learning behavior habit can improve the learning efficiency of the collaborative learning group members, and the knowledge points as much as possible can help the group members to learn more knowledge and skills. The collaborative learning group formed by the grouping method is more efficient. The application has the advantages of high grouping efficiency, large grouping benefit, wide application range and the like, and can be used for learning resource recommendation, such as course recommendation, and can also be used for grouping activities in other fields.

Disclosed is an e-commerce platform that provides matching-based services between supplier members and purchaser members. The e-commerce platform, which mediates transactions between a supplier member and a purchaser member, comprises a processing server that performs: a matching task for calculating a matching score between a supplier member and a purchaser member on the basis of a similarity analysis between registered products registered by the supplier member and a product to be purchased by the purchaser member, and for querying the supplier member as to whether to accept establishment of a one-to-one relationship with the purchaser member on the basis of the calculated matching score; and a feedback-providing task of providing feedback reflecting purchase trends for registered products and detailed information thereof registered by the supplier member.

The disclosure provides a diversity perception model training method and device, and a recommendation ranking method and device. It relates to the technical field of artificial intelligence, and in particular to the technical fields of big data processing, deep learning, intelligent recommendation, etc. The specific implementation scheme is as follows: a plurality of samples and a plurality of sample corresponding perception labels are obtained; the user features, resource features and diversity perception features corresponding to the plurality of samples are input into a to-be-trained model to obtain the perception values of the users to the resources corresponding to the plurality of samples; and the to-be-trained model is trained according to the perception values and the perception labels corresponding to the plurality of samples to obtain a diversity perception model. According to the scheme of the disclosure, the diversity perception model can predict the diversity perception of different users to resources, thereby improving the accuracy of recommendations.

The application discloses an industrial plant structure scheme engineering cost comparison and measurement method, and belongs to the technical field of building engineering cost management; the application comprises target industrial plant alternative structure schemes to be compared, collects building information model design data corresponding to each set of alternative structure schemes, and collects core feature data of the corresponding industrial plant; component analysis processing is performed on the building information model design data of each set of alternative structure schemes, automatic analysis of engineering quantity is performed, and sub-item engineering quantity detail data corresponding to each set of alternative structure schemes is generated; according to the application, engineering quantity calculation error is reduced from 5-8% in a traditional method to 1-2% through automatic extraction of BIM model data, measurement reliability is greatly improved, whole life cycle cost analysis covers whole-process economy evaluation from construction to operation, and long-term operation burden caused by short-term cost optimization is avoided.

A concept tagging model is trained based at least in part on a result of a large language model used to analyze training data. Text content of a website is received. The concept tagging model is applied to the text content to identify one or more concept terms in the text content. An embedding model is applied to the identified one or more concept terms to determine one or more embeddings. Using the one or more embeddings, a closest matching granular category in a taxonomy of categories is identified for the website.

Provided is an anomaly detection device comprising: a time series data acquisition unit that acquires time series data from a plurality of sensors included in a system; a calculation unit that calculates the degree of abnormality of the sensors according to a comparison between time series segments into which the time series data acquired by the time series data acquisition unit is divided and a representative segment which is a representative point selected in advance from time series data for learning; a change detection unit that detects a change in the state of the system; and a representative segment updating unit that, according to the result of the detection by the change detection unit, uses time series data acquired after the detection of the change to update the representative segment. When the change detection unit has detected a change in the state of the system, the calculation unit compares the time series segments with the updated representative segment to calculate the degree of abnormality. With this configuration, it is possible to properly calculate the degree of abnormality even when the state of the system changes and it is possible to properly support a user in decision making.

This invention discloses a cross-business data collaborative analysis method and system based on multimodal AI, belonging to the field of artificial intelligence technology. It solves the technical problems of bridging the semantic gap between multiple business systems and the inability to quantify differences in business perspectives in existing technologies. The method includes: constructing a unified cross-business semantic space containing atomic event types and entity types and assigning prototype vectors; inputting multimodal data into a base matching network to obtain base matching vectors, querying a perspective correction parameter set from a representation difference mapping table based on business identifiers, and inputting it into a perspective correction network to obtain corrected semantic representation vectors; parsing the collaborative analysis task to obtain target types and retrieving associated vectors; constructing a cross-business semantic association graph using the associated vectors as nodes, identifying key paths connecting different business systems, and outputting the collaborative analysis results after differentiation and fusion through a graph neural network. This invention achieves semantic alignment and perspective-aware collaborative analysis across business systems.

Some aspects relate to technologies for generating pivots using a generative model and grouping items into pivot groups using the pivots. Pivots are generated by obtaining item information for items, generating a prompt using the item information, and causing a generative model to use the prompt to generate text for the pivots, including a pivot name and pivot description for each pivot. A pivot embedding is also generated for each pivot. When items are to be returned to a user device (e.g., as recommended items or search result items), item embeddings for the items and the pivot embeddings for the pivots are used to assign each item to a particular pivot to generate pivot groups that each includes a pivot name, a pivot description, and assigned items. User interface information is provided to present at least a portion of the pivot groups on a user device.

In the field of artificial intelligence technology, this invention specifically relates to an attribute word mining method and related products. The method includes: acquiring candidate words related to the attribute parameters of the object being mined; clustering the candidate words to obtain clusters composed of the candidate words; determining a ranking score for each cluster based on the consistency of the topic distribution of the candidate words within the cluster, wherein the ranking score is positively correlated with the consistency of the topic distribution of the candidate words within the cluster; ranking the clusters according to their ranking scores; and filtering the attribute words of the object being mined based on the ranking results. This application can reduce the difficulty of attribute word mining and improve its accuracy and efficiency.

The application provides a data processing method and device, a computer program product and a readable storage medium, and relates to the technical field of data processing. A data processing device obtains first application data of a first terminal, and the first application data is used to indicate the type of an application program possessed by the first terminal. The data processing device processes the first application data of the first terminal by using a first model, so that a first user type corresponding to the first terminal can be obtained. The data processing device can send first push information matched with the first user type to a user of the first terminal based on the obtained first user type corresponding to the first terminal. The data processing device predicts the first user type of the user of the first terminal according to the first application data of the first terminal on the premise of protecting the privacy of the user of the terminal, pushes information interested by the user of the first terminal to the user, realizes accurate push of information, and improves the user experience.

Embodiments of this disclosure relate to symbolic access and methods for industrial equipment systems based on externally deployed gateway devices. Embodiments of this disclosure may include a system performing operations including: receiving a request from a requesting device for access to data associated with industrial automation equipment, and identifying the industrial automation equipment based on the request. The operations may include sending a query for template data to the industrial automation equipment based on the request and receiving the template data. The operations may include determining a data structure based on the requesting device and generating a data structure based on the template data and a mapping between the data structure and the template data. The operations may additionally include sending the data structure to the requesting device.

An information handling system includes a data store configured to store an account associated with a first alert and a second alert. A processor may receive the first alert and the second alert, map the first alert and the second alert to a first node and a second node of a causality graph, traverse the causality graph starting from the sink node to the source node to determine an association between the first alert and the second alert, and generate the account based on the association between the first alert and the second alert.

This application relates to the field of data mining technology, specifically to an intelligent topic recommendation method, apparatus, device, and medium, used to address the problem of low user response rates in traditional topic recommendation methods. The method involves: based on the target user's user information and topic filtering conditions, obtaining various topic categories for communication with the target user; and based on each topic category, a topic library, and preset importance of each topic category, obtaining a predicted score for each topic content within each category; then, sorting the predicted scores for each topic content to obtain the target topic content for communication with the target user. The topic library includes each topic content within each topic category and the rules for determining the predicted score; a higher predicted score indicates a higher response rate for the topic content. Thus, using the target topic content for communication can effectively attract the target user and improve the user response rate.

This invention relates to the field of cross-modal hashing technology and discloses a multimodal hash retrieval method, device, electronic device, and storage medium. The method includes: constructing a sample training set based on several training samples and sample labels; inputting the sample training set into a preset initial hash retrieval model to obtain a fusion feature vector by extracting and fusing the modal data corresponding to each training sample to obtain a classification confidence score; then dynamically weighting the fusion feature vector using the classification confidence score to obtain a refined category prototype, thereby obtaining a predicted probability distribution corresponding to each training sample; then obtaining a model loss based on the predicted probability distribution, sample labels, and refined category prototypes; optimizing the initial hash retrieval model using the model loss to obtain a target hash retrieval model; and finally, using the target hash retrieval model to retrieve the data to be retrieved. This approach improves the model's robustness to multi-label noise while maintaining high retrieval accuracy.

This invention provides an emergency power supply operation scheduling and resource allocation system for photovoltaic street light clusters, relating to the field of data processing technology. The system is used to: decompose street light nodes and vehicles into supply segments, transfer segments, and receiving segments; establish transmission paths for transfer segments that meet the connection sequence according to load priority; identify repeatedly called supply segments and transfer segments in different transmission paths; extract the participation value of the supply segment segment segment by segment along the starting direction of each independent chain segment; identify the sustainable supply capacity of the transmission path; identify the demand priority of the path; obtain the path receiving value; divide chain segments from the same supply source into main chain segments and auxiliary chain segments and reorganize them in sequence; determine the resource allocation order for objects with different priorities; and obtain the resource allocation result. This invention can achieve resource collaborative scheduling across nodes and across carriers.

The application relates to an abnormal operation information determination method and device, computer equipment and a storage medium. The application relates to the technical field of artificial intelligence. The method comprises the following steps: acquiring comprehensive operation time of a target host and a plurality of operation data, and identifying an operation path corresponding to each operation data; screening a key operation path, and identifying core batch operation time of the key operation path based on the comprehensive operation time of the target host; when the time is greater than a preset operation time threshold, identifying a time abnormality reason of the key operation path, and otherwise, identifying abnormal operation data in the key operation path; identifying abnormal operation information of the key operation path based on the time abnormality reason of the key operation path or the abnormal operation data of the key operation path, and taking the abnormal operation information as target abnormal operation information of the target host. The method can improve the efficiency of analyzing abnormal operation information of a key operation path of a host batch.

The present disclosure relates to the construction and application of a music score training database. A method for constructing a music score training database is proposed, which comprises, for each level of the music score training database, obtaining a first label (L1) generated by machine learning based on a set of music features pre-set for the music score training database to be constructed; obtaining a second label (L1H) generated based on the set of music features and at least one second sub-label (L2H) contained in the second label (L1H); generating candidate music samples according to the combination of music features specified by the second sub-label (L2H) and the range of music feature values, verifying at least one of the first label (L1) and the second label (L1H) using the generated candidate music samples, wherein the specification of the at least one of the first label (L1) and the second label (L1H) can be adjusted based on the verification result; and constructing the music score training database based on the candidate music samples that pass the verification and their corresponding labels.

The data processing method and device provided in the application relate to the technical field of data analysis. The data processing method can accurately match the main business information of the enterprise to be warehoused with the green industry type in the green enterprise database, and then, the reliability of the class label corresponding to the matching degree of the enterprise to be warehoused and the corresponding green industry type is also high. In this way, when the project to be evaluated needs to be evaluated, the evaluation rule corresponding to the category of the project to be evaluated can be obtained from the database according to the class label, so as to evaluate the project to be evaluated. Since the evaluation rule matches the project to be evaluated, the accuracy is high and the efficiency is high.

Described herein is an implementation of a classification of content collection using process supervision with a chain-of-thought reasoning capable machine-learned (ML) model. In an example aspect, a process supervision system includes processing circuitry and memory storing a content source of a ML model. The content source includes text-labeled content of a first type. The processing circuitry is configured to obtain a collection of multiple first-typed content items of the content source. The collection has text metadata associated therewith. The processing circuitry is further configured to produce supervised fine-tuning (SFT) queries regarding the collection based, at least in part, on one or more of the first-typed content items of the collection and / or the text metadata of the collection. The processing circuitry is still further configured to, based on the SFT queries, assemble a chain-of-thought reasoning request for submission to a chain-of-thought reasoning capable ML classifier.

The system according to this embodiment aims to efficiently manage a large volume of incoming emails and to respond quickly to important emails. [Solution] The system according to the embodiment comprises an acquisition unit, a classification unit, a summary generation unit, a reply generation unit, and a transmission unit. The acquisition unit acquires incoming emails. The classification unit classifies the emails acquired by the acquisition unit. The summary generation unit summarizes the content of the emails classified by the classification unit. The reply generation unit generates a reply to the emails summarized by the summary generation unit. The transmission unit transmits the reply generated by the reply generation unit.

The application relates to the field of engineering rock mass simulation technology, and discloses an engineering rock mass data-driven calculation method and system based on in-situ test, which utilizes a stress-strain database obtained on site, a conservation law and boundary conditions to perform calculation, is not affected by the scale of the engineering rock mass, can greatly improve the simulation and prediction capability of the engineering rock mass, and the calculation result is more consistent with the actual situation.